Systems and methods for assessing a facility based on audio/visual delta analysis

ABSTRACT

In one embodiment, a system may include a multi-purpose sensor coupled to a machine operating in an industrial environment. The multi-purpose sensor may include a camera that obtains a first and second set of image data including images of the machine and an environment surrounding the machine. The first set of image data is associated with a baseline of the machine and the environment, and the second set of image data is acquired subsequent to when the first set is acquired. The system may include a computing device that may include a processor to receive the first and second set of image data, determine baseline positions of objects in the first set, determine subsequent positions of the objects in the second set, determine whether the subsequent positions vary from the baseline positions, and perform an action when the subsequent positions vary from the baseline positions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from and the benefit of U.S.Provisional Application Ser. No. 62/139,182, entitled “Systems andMethods for Exchanging Information Between Devices in an IndustrialAutomation Environment,” filed Mar. 27, 2015, which is herebyincorporated by reference in its entirety.

BACKGROUND

The present disclosure generally relates to an industrial automationsystem. More particularly, the present disclosure relates to systems andmethods for a multi-purpose sensing device for industrial automationequipment, such as a machine, that is associated with the industrialautomation system.

BRIEF DESCRIPTION

In one embodiment, a system may include a multi-purpose sensor that maycouple to a machine operating in an industrial environment. Themulti-purpose sensor may include a camera that may obtain a first set ofimage data and a second set of image data including images of themachine and an environment surrounding the machine. The first set ofimage data is associated with a baseline of the machine and theenvironment, and the second set of image data is acquired subsequent towhen the first set of image data is acquired. The system may include acomputing device communicatively coupled to the multi-purpose sensor.The computing device may include a processor that may receive the firstset of image data and the second set of image data from themulti-purpose sensor, determine one or more baseline positions of one ormore objects in the first set of image data, determine one or moresubsequent positions of the one or more objects in the second set ofimage data, determine whether the subsequent positions vary from thebaseline positions, perform an action when the subsequent positions varyfrom the baseline positions.

In one embodiment, a tangible, non-transitory computer readable mediummay store instructions that, when executed by a processor, may cause theprocessor to receive a first set of image data and a second set of imagedata from a multi-purpose sensor. The first and second sets of imagedata are obtained by a camera of the multi-purpose sensor, the first andsecond sets of image data include an image of a machine in an industrialautomation application, an image of an environment surrounding themachine, or both, and the second set of image data is acquiredsubsequent to when the first set of image data is acquired. Theinstructions may also cause the processor to determine one or morebaseline positions of one or more objects in the first set of imagedata, determine one or more subsequent positions of the one or moreobjects in the second set of image data, determine whether thesubsequent positions vary from the baseline positions, and perform anaction when the subsequent positions vary from the baseline positions.

In one embodiment, a method may include receiving, via a processor, afirst set of image data and a second set of image data from amulti-purpose sensor. The first and second sets of image data areobtained by a camera of the multi-purpose sensor, the first and secondsets of image data include an image of a machine in an industrialautomation application, an environment surrounding the machine, or both,and the second set of image data is acquired subsequent to when thefirst set of image data is acquired. The method may also includedetermining, via the processor, one or more baseline positions of one ormore objects in the first set of image data, determining, via theprocessor, one or more subsequent positions of the one or more objectsin the second set of image data, determining, via the processor, whetherthe subsequent positions vary from the baseline positions, andperforming, via the processor, an action when the subsequent positionsvary from the baseline positions by a sufficient threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a diagrammatical representation of an exemplary control andmonitoring system, in accordance with embodiments presented herein;

FIG. 2 is a schematic representation of a communication network, inaccordance with embodiments presented herein;

FIG. 3 is a block diagram of example components within a computingdevice that is part of the communication network of FIG. 2, inaccordance with embodiments presented herein;

FIG. 4 is a block diagram of example components within a cloud-basedcomputing system of the communication network of FIG. 2, in accordancewith embodiments presented herein;

FIG. 5 is a schematic diagram of an industrial automation systemincluding a multi-purpose sensing device, in accordance with embodimentspresented herein;

FIG. 6 is a schematic diagram of example components of the multi-purposesensing device of FIG. 5, in accordance with embodiments presentedherein;

FIG. 7 is a flow diagram of a method for performing a preventativeaction when measurements are outside a range of expected measurements,in accordance with embodiments presented herein;

FIG. 8 is a flow diagram of a method for determining directions to aphysical location of the multi-purpose sensing device and/or a machineto which the multi-purpose sensing device is attached, in accordancewith embodiments presented herein;

FIG. 9 is a flow diagram of a method for performing a preventativeaction based on the location of a technician, in accordance withembodiments presented herein;

FIG. 10 is a flow diagram of a method for performing a preventativeaction based on operational parameters, in accordance with embodimentspresented herein;

FIG. 11 is a flow diagram of a method for comparing signatures based onsensor measurements for various periods of time, in accordance withembodiments presented herein;

FIG. 12 is a flow diagram of a method for operating industrialautomation equipment productively based on sensor data and/orimage/video data, in accordance with embodiments presented herein;

FIG. 13 is a flow diagram of a method for comparing positions of objectsbased on image/video data for various periods of time, in accordancewith embodiments presented herein;

FIG. 14 is a screen capture of a display showing baseline image/videodata 168, in accordance with embodiments presented herein; and

FIG. 15 is a screen capture of the display showing modified image/videodata 170, in accordance with embodiments presented herein.

DETAILED DESCRIPTION

One or more specific embodiments will be described below. In an effortto provide a concise description of these embodiments, not all featuresof an actual implementation are described in the specification. Itshould be appreciated that in the development of any such actualimplementation, as in any engineering or design project, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

Generally, the present disclosure discusses numerous concepts regardinghow devices in an industrial automation system may exchange informationwith each other and use this shared information to assist users in theindustrial automation environment to manage the operations andmaintenance of the devices. In one embodiment, the industrial automationsystem may include a communication architecture that is structuredaccording to a tri-partite paradigm that facilitates communicationsbetween a device, a computing device, and a cloud-based computingsystem. The information shared between each component within thistri-partite structure may enable various devices within the industrialautomation system to operate more efficiently, users to perform tasksrelated to the conditions or operations of the industrial automationsystem more efficiently, and generally provide for improved operationsof the industrial automation system. In addition, the information sharedwithin the tri-partite structure may include data obtained via amulti-purpose sensing device attached to an industrial component (e.g.,a machine) associated with the industrial automation system. The datamay be used to determine operational parameters of the industrialcomponent, parameters of the environment around the industrialcomponent, directions to a physical location of the multi-purposesensing device and/or the industrial component, a location of atechnician relative to the industrial component, or the like.

Numerous sensors are often used to monitor one or more properties of anindustrial automation device (e.g., a machine) used in an industrialautomation system. The types of sensors may include vibration,temperature, motion, sound, pressure, and so forth. Generally, thesensors may be individual devices that serve a single purpose. Also, thesensors are often disposed in various locations on or around theindustrial automation device, thereby taking up a certain amount ofphysical space on or within the device where physical space may belimited. In some instances, the sensors may only communicate with otherdevices via a wired connection. In this case, wiring complexity mayincrease in proportion to the number of sensors used on the device.Further, the sensors may be small in size and difficult to locate on thedevices when a technician attempts to service and/or replace the sensorsand/or the machine the sensors are monitoring.

Accordingly, embodiments of the present disclosure relate to providing amulti-purpose sensing device that may be attached to an industrialautomation device. “Multi-purpose,” as used herein, may refer to thesensing device serving multiple purposes, including enhanced sensing ofproperties of the machine and the environment surrounding the machineusing a number of sensors in one device. In one embodiment, amulti-purpose sensing device may enable a computing device to determinedirections to the multi-purpose sensing device and/or the machine, amongother things described below. In some embodiments, the multi-purposesensing device may include a number of gas sensors (e.g., oxygen),temperature sensors, motion sensors, vibration sensors, sound sensors,and the like consolidated in a single device.

Also, the multi-purpose sensing device may include a processor that mayreceive data from each of the sensors and may process the data forvarious types of analysis. For example, the multi-purpose sensing devicemay continuously monitor certain properties associated with theindustrial automation device and the environment surrounding the deviceover time to determine whether the industrial automation device and/orthe environment surrounding the device is suitable for the purposes ofthe corresponding industrial automation system. That is, whenmeasurements acquired by the multi-purpose sensing device are outside arange of expected measurements, the processor may perform a preventativeaction, such as send a notification to a technician and/or send acommand to the industrial automation device. Additionally, the processormay determine one or more baseline signatures based on the sensormeasurements over periods of time and compare the baseline signatures toother signatures determined based on sensor measurements taken later. Inthis way, the processor may analyze a measurement from one sensor withrespect to the measurements from the other sensors at various modes ofoperation of the machine. When the signatures vary, the processor mayperform a preventative action, diagnostics, and/or predictive behavior.As such, the multi-purpose sensing device may include a wirelesscommunication component (e.g., antenna) that enables wirelesslytransmitting and receiving data. As a result, the multi-purpose sensingdevice may reduce wiring complexity by using wireless communication.

In some instances, locating sensors in an industrial automation systemmay be difficult due to the size and/or arrangement of the devices usedin the system. Accordingly, in some embodiments, the multi-purposesensing device may also include a location beacon or tag that may beused to enable a computing device (e.g., tablet, smartphone) todetermine navigational directions to the multi-purpose sensing deviceand/or the respective industrial automation device associated with themulti-purpose sensing device. Additionally, some embodiments may includethe multi-purpose sensing device, the computing device, a cloud-basedcomputing system, or the like using proximity sensing techniques todetermine location information related to neighboring devices based ondata obtained by the multi-purpose sensing device, the computing device,the cloud-based computing system, and the like.

Further beneficial techniques provided by the multi-purpose sensingdevice may include performing a preventative action, such as disablingcertain steps of procedures (e.g., lockout-tagout) displayed by softwareon the industrial automation device, based on the location of thetechnician relative to the industrial automation device. For example,certain operations performed by technicians, machinery, and/or processesin a factory, plant, and the like, may involve performing certainprocedures to ensure that the operations are executed properly. Whenpreparing to service an industrial automation device (e.g., drive), atechnician may follow a lockout-tagout procedure associated with placingthe particular industrial automation device offline before performingthe service operations. Here, if the multi-purpose sensing devicedetermines that certain conditions exist with the industrial automationdevice or the surrounding environment to place the industrial automationdevice offline, the multi-purpose sensing device may disable certainoperational functions of a computing device used by the technicianattempting to place the industrial automation device offline.

In some embodiments, a tri-partite paradigm or communication network mayenable at least three types of components to exchange informationregarding an industrial automation system between each other moreefficiently. The components of the communication network may include, inone example, the computing device, the device or machine operating in anindustrial automation system, and the cloud-based computing systemcommunicatively coupled to the computing device and the device in theindustrial automation system. In one embodiment, this tri-partiteparadigm may involve a software application operating on a computingdevice, such that the software application may monitor, control, access,or view an automation device in the industrial automation system. Thecomputing device may use the data sent from the multi-purpose sensingdevice to control the operation of the software application used toassist the technician with regard to the maintenance and operation ofequipment in an industrial automation system and/or to control theoperation of equipment of the industrial automation system. In someembodiments, the software may assist the technician to perform alockout-tagout procedure, which may detail how to place equipmentoffline to enable the technician to perform maintenance on therespective equipment.

As discussed herein, lockout-tagout procedures are used throughoutvarious industries and research settings to ensure that machinery and/orprocesses are placed offline properly and not started prior to thecompletion of maintenance or servicing. Generally, a lockout-tagoutprocedure may include physically locking a part of the machinery in oneposition (e.g., off) to prevent the part from shifting to an alternateposition (e.g., on). The procedure may then involve tagging or placing alabel on the device to indicate that the machinery is locked out or isbeing serviced. Typically, the tag may include information such as astatement (e.g., “do not operate—equipment locked out”) and theinformation of the person who affixed the tag to the device.

Using the data from the multi-purpose sensing device, the softwareapplication may cause certain steps of the lockout-tagout procedure tobe performed via the computing device to be disabled when the dataindicates measurements are not within the range of expectedmeasurements. In addition, the software may use the location data toprovide navigational directions to certain industrial automation devicesassociated with particular steps of the lockout-tagout procedure basedon data received from the multi-purpose sensing device.

FIG. 1 is a diagrammatical representation of an exemplary control andmonitoring system 10, in accordance with embodiments presented herein.In FIG. 1, the control and monitoring system 10 is illustrated asincluding a human machine interface (HMI) 12 and a control/monitoringdevice or automation controller 14 adapted to interface with devicesthat may monitor and control various types of industrial automationequipment 16. It should be noted that such an interface in accordancewith embodiments of the present techniques may be facilitated by the useof certain network strategies. Indeed, an industry standard network maybe employed, such as DeviceNet, to enable data transfer. Such networkspermit the exchange of data in accordance with a predefined protocol,and may provide power for operation of networked elements. Although notdepicted in FIG. 1, the control and monitoring system 10 may alsoinclude controllers, input/output (I/O) modules, motor control centers,operator interfaces, contactors, starters, drives, relays, networkswitches (e.g., Ethernet switches, modular-managed, fixed-managed,service-router, industrial, unmanaged, etc.), and the like.

The industrial automation equipment 16 may take many forms and includedevices for accomplishing many different and varied purposes. Forexample, the industrial automation equipment 16 may include machineryused to perform various operations in a compressor station, an oilrefinery, a batch operation for making food items, a mechanized assemblyline, and so forth. Accordingly, the industrial automation equipment 16may comprise a variety of operational components, such as electricmotors, valves, actuators, temperature elements, pressure sensors, or amyriad of machinery or devices used for manufacturing, processing,material handling and other applications.

Additionally, the industrial automation equipment 16 may include varioustypes of equipment that may be used to perform the various operationsthat may be part of an industrial application. For instance, theindustrial automation equipment 16 may include electrical equipment,hydraulic equipment, compressed air equipment, steam equipment,mechanical tools, protective equipment, refrigeration equipment, powerlines, hydraulic lines, steam lines, and the like. Some example types ofequipment may include mixers, machine conveyors, tanks, skids,specialized original equipment manufacturer machines, and the like. Inaddition to the equipment described above, the industrial automationequipment 16 may also include motors, protection devices, switchgear,compressors, and the like.

In certain embodiments, one or more properties of the industrialautomation equipment 16 may be monitored and controlled by certainequipment for regulating control variables. For example, sensors 18 andactuators 20 may monitor various properties of the industrial automationequipment 16 and may be involved to adjust operations of the industrialautomation equipment 16, respectively.

In some cases, the industrial automation equipment 16 may be associatedwith devices used by other equipment. For instance, scanners, gauges,valves, flow meters, and the like may be disposed on industrialautomation equipment 16. Here, the industrial automation equipment 16may receive data from the associated devices and use the data to performtheir respective operations more efficiently. For example, a controller(e.g., control/monitoring device 14) of a motor drive may receive dataregarding a temperature of a connected motor and may adjust operationsof the motor drive based on the data.

In certain embodiments, the industrial automation equipment 16 and/orthe control/monitoring device 14 may include a computing device and/or acommunication component that enables the industrial equipment 16 tocommunicate data between each other and other devices. The communicationcomponent may include a network interface that may enable the industrialautomation equipment 16 to communicate via various protocols such asEtherNet/IP®, ControlNet®, DeviceNet®, or any other industrialcommunication network protocol. Alternatively, the communicationcomponent may enable the industrial automation equipment 16 tocommunicate via various wired or wireless communication protocols, suchas Wi-Fi, mobile telecommunications technology (e.g., 2G, 3G, 4G, LTE),Bluetooth®, near-field communications technology, and the like.

The sensors 18 may be any number of devices adapted to provideinformation regarding process conditions. The actuators 20 may includeany number of devices adapted to perform a mechanical action in responseto a signal from a controller (e.g., the automation controller 14). Thesensors 18 and actuators 20 may be utilized to operate the industrialautomation equipment 16. Indeed, they may be utilized within processloops that are monitored and controlled by the control/monitoring device14 and/or the HMI 12. Such a process loop may be activated based onprocess inputs (e.g., input from a sensor 18) or direct operator inputreceived through the HMI 12. As illustrated, the sensors 18 andactuators 20 are in communication with the control/monitoring device 14.Further, the sensors 18 and actuators 20 may be assigned a particularaddress in the control/monitoring device 14 and receive power from thecontrol/monitoring device 14 or attached modules.

Input/output (I/O) modules 22 may be added or removed from the controland monitoring system 10 via expansion slots, bays or other suitablemechanisms. In certain embodiments, the I/O modules 22 may be includedto add functionality to the control/monitoring device 14, or toaccommodate additional process features. For instance, the I/O modules22 may communicate with new sensors 18 or actuators 20 added to monitorand control the industrial automation equipment 16. It should be notedthat the I/O modules 22 may communicate directly to sensors 18 oractuators 20 through hardwired connections or may communicate throughwired or wireless sensor networks, such as Hart or IOLink.

Generally, the I/O modules 22 serve as an electrical interface to thecontrol/monitoring device 14 and may be located proximate or remote fromthe control/monitoring device 14, including remote network interfaces toassociated systems. In such embodiments, data may be communicated withremote modules over a common communication link, or network, whereinmodules on the network communicate via a standard communicationsprotocol. Many industrial controllers can communicate via networktechnologies such as Ethernet (e.g., IEEE802.3, TCP/IP, UDP,EtherNet/IP®, and so forth), ControlNet®, DeviceNet® or other networkprotocols (Foundation Fieldbus (H1 and Fast Ethernet) Modbus TCP,Profibus) and also communicate to higher level computing systems.

In the illustrated embodiment, several of the I/O modules 22 areconfigured to transfer input and output signals between thecontrol/monitoring device 14 and the industrial automation equipment 16.As illustrated, the sensors 18 and actuators 20 may communicate with thecontrol/monitoring device 14 via one or more of the I/O modules 22coupled to the control/monitoring device 14.

In certain embodiments, the control/monitoring system 10 (e.g., the HMI12, the control/monitoring device 14, the sensors 18, the actuators 20,the I/O modules 22) and the industrial automation equipment 16 may makeup an industrial application 24. The industrial application 24 mayinvolve any type of industrial process or system used to manufacture,produce, process, or package various types of items. For example, theindustrial applications 24 may include industries such as materialhandling, packaging industries, manufacturing, processing, batchprocessing, and the like.

In certain embodiments, the control/monitoring device 14 may becommunicatively coupled to a computing device 26 and a cloud-basedcomputing system 28. In this network, input and output signals generatedfrom the control/monitoring device 14 may be communicated between thecomputing device 26 and the cloud-based computing system 28.

FIG. 2 is a schematic representation of a communication network 30 thatenables devices to communicate with each other within an industrialapplication, in accordance with embodiments presented herein. As such,the communication network 30 enables devices that are part of theindustrial application 24 to communicate with each other and with otherdevices that are not part of the industrial application 24. As mentionedabove, the industrial application 24 may be in the material handling,packaging industries, manufacturing, processing, batch processing, orany technical field that employs the use of the industrial automationequipment 16.

With the foregoing in mind, in one embodiment, data acquired by theindustrial automation equipment 16 may be transmitted to a computingdevice 26. The computing device 26 may be a computing device that mayinclude communication abilities, processing abilities, and the like. Forexample, the computing device 26 may be any general computing devicethat may monitor, control, and/or operate one or more of the industrialautomation equipment 16. As such, the computing device 26 may be alaptop computer, a tablet computer, a mobile phone device computingdevice, a general personal computer, a wearable computing device, or thelike. Additional details regarding the computing device 26 will bediscussed below with reference to FIG. 3.

In addition to communicating with the industrial automation equipment16, the computing device 26 may also communicate with the cloud-basedcomputing system 28. The cloud-based computing system 28 may be acloud-accessible platform that may include one or more servers, one ormore computing devices (e.g., general purpose computers), and the like.In any case, the cloud-based computing system 28 may include a number ofcomputers that may be connected through a real-time communicationnetwork, such as the Internet, Ethernet, EtherNet/IP, ControlNet, or thelike, such that the multiple computers may operate together as a singleentity. The real-time communication network may include any network thatenables various devices to communicate with each other at near real-timeor such that data is communicated with each other at near instantaneousspeeds. In one embodiment, the cloud-based computing system 28 may becapable of communicating with the industrial automation equipment 16 andthe computing device 26. As such, the cloud-based computing system 28may be capable of wired or wireless communication between the industrialautomation equipment 16 and the computing device 26. In one embodiment,the cloud-based computing system 28 may be accessible via the Internetor some other network.

After establishing a communication connection between the computingdevice 26 and the industrial automation equipment 16 (e.g., via anassociated control/monitoring device 14 or computing device of theindustrial automation equipment 16), the cloud-based computing system 28may receive data acquired by the computing device 26 and the industrialautomation equipment 16. After receiving this data, in one embodiment,the cloud-based computing system 28 may perform large-scale dataanalysis operations on the data, such that the operations may bedistributed over the computers that make up the cloud-based computingsystem 28.

In another embodiment, the cloud-based computing system 28 may forwardacquired data or analyzed data to different computing devices, variousindustrial automation equipment, or the like. As such, the cloud-basedcomputing system 28 may maintain a communication connection with variousindustrial automation equipment 16, computing devices 26, and the like.Additional details regarding the cloud-based computing system 28 will bediscussed below with reference to FIG. 4.

FIG. 3 is a block diagram of example components within the computingdevice 26 that is part of the communication network 30 of FIG. 2, inaccordance with embodiments presented herein. For example, the computingdevice 26 may include a communication component 35, a processor 36, amemory 37, a storage 38, input/output (I/O) ports 39, an image sensor 40(e.g., a camera), a location sensor 41, a display 42, additional sensors(e.g., vibration sensors, temperature sensors), and the like. Thecommunication component 35 may be a wireless or wired communicationcomponent that may facilitate communication between the industrialautomation equipment 16, the cloud-based computing system 28, and othercommunication capable devices (e.g., multi-purpose sensing devicedescribed below).

The processor 36 may be any type of computer processor or microprocessorcapable of executing computer-executable code. The processor 36 may alsoinclude multiple processors that may perform the operations describedbelow. The memory 37 and the storage 38 may be any suitable articles ofmanufacture that can serve as media to store processor-executable code,data, or the like. These articles of manufacture may representcomputer-readable media (e.g., any suitable form of memory or storage)that may store the processor-executable code used by the processor 36 toperform the presently disclosed techniques. Generally, the processor 36may execute software applications that include programs that enable auser to track and/or monitor operations of the industrial automationequipment 16 via a local or remote communication link. That is, thesoftware applications may communicate with the control/monitoring device14 and gather information associated with the industrial automationequipment 16 as determined by the control/monitoring device 14, viasensors disposed on the industrial automation equipment 16, and thelike.

The memory 37 and the storage 38 may also be used to store the data,analysis of the data, the software applications, and the like. Thememory 37 and the storage 38 may represent non-transitorycomputer-readable media (e.g., any suitable form of memory or storage)that may store the processor-executable code used by the processor 36 toperform various techniques described herein. It should be noted thatnon-transitory merely indicates that the media is tangible and not asignal.

In one embodiment, the memory 37 and/or storage 38 may include asoftware application that may be executed by the processor 36 and may beused to monitor, control, access, or view one of the industrialautomation equipment 16. As such, the computing device 26 maycommunicatively couple to industrial automation equipment 16 or to arespective computing device of the industrial automation equipment 16via a direct connection between the two respective devices or via thecloud-based computing system 28. Additionally, the memory 37 may be usedto store the expected ranges for the various sensor measurements, aswell as the baseline measurement signatures (e.g., for individual sensormeasurements and for collective measurements including more than onesensor measurement) for different periods of time (e.g., during phasesor modes of operation of the industrial automation equipment 16),discussed below. Further, the memory 37 may store information regardingvarious reasons associated with varying measurement signatures atdifferent times of operation to enable the processor 36 to determinewhat preventative actions to take, to diagnose the cause of thevariance, and/or to predict what other industrial automation equipment16 may have issues if the monitored industrial automation equipment 16continues to operate in the current state.

The I/O ports 39 may be interfaces that may couple to other peripheralcomponents such as input devices (e.g., keyboard, mouse), sensors,input/output (I/O) modules, and the like. I/O modules may enable thecomputing device 26 to communicate with the industrial automationequipment 16 or other devices in the industrial automation system viathe I/O modules.

The image sensor 40 may include any image acquisition circuitry such asa digital camera capable of acquiring digital images, digital videos, orthe like. The location sensor 41 may include circuitry designed todetermine a physical location of the computing device 26. In oneembodiment, the location sensor 41 may include a global positioningsystem (GPS) sensor that acquires GPS coordinates for the computingdevice 26. In another embodiment, the location sensor 41 may includeother circuitry such as a radio wave transmitter, an infrared sensor,and the like that may acquire data that may be used to determine alocation of the computing device 26 with respect to other industrialautomation equipment 16 or other fixtures in the industrial automationsystem. In certain embodiments, the computing device 26 may also includevarious other sensors that may provide additional data related to anenvironment in which the computing device 26 exists. For instance, theother sensors may include an accelerometer, a gas (e.g., smoke, carbonmonoxide) sensor, or the like.

The display 42 may depict visualizations associated with software orexecutable code being processed by the processor 36. In one embodiment,the display 42 may be a touch display capable of receiving inputs from auser of the computing device 26. As such, the display 42 may serve as auser interface to communicate with the industrial automation equipment16. The display 42 may be used to display a graphical user interface(GUI) for operating the industrial automation equipment 16, for trackingthe maintenance of the industrial automation equipment 16, performingvarious procedures (e.g., lockout tagout, placing device offline,replacing component, servicing device) for the industrial automationequipment 16, and the like. The display 42 may be any suitable type ofdisplay, such as a liquid crystal display (LCD), plasma display, or anorganic light emitting diode (OLED) display, for example. Additionally,in one embodiment, the display 42 may be provided in conjunction with atouch-sensitive mechanism (e.g., a touch screen) that may function aspart of a control interface for the industrial equipment 16. In someembodiments, the operator interface may be characterized as the HMI 12,a human-interface machine, or the like.

Although the components described above have been discussed with regardto the computing device 26, it should be noted that similar componentsmay make up the control/monitoring device 14. Moreover, the computingdevice 26 may also be part of the industrial automation equipment 16,and thus may monitor and control certain operations of the industrialautomation equipment 16. Further, it should be noted that the listedcomponents are provided as example components and the embodimentsdescribed herein are not to be limited to the components described withreference to FIG. 3.

FIG. 4 is a block diagram of example components within the cloud-basedcomputing system 28 of the communication network 30 of FIG. 2, inaccordance with embodiments presented herein. As mentioned above, thecloud-based computing system 28 may include a number of computingdevices, such as servers 43 that may be communicatively coupled to eachother and may distribute various tasks between each other to perform thetasks more efficiently. In certain embodiments, each server 43 mayinclude the example components described above as part of the computingdevice 26 in FIG. 3.

The cloud-based computing system 28 may also have access to a number ofdatabases 44. The databases 44 may be related to various aspects of theindustrial automation system, the industrial automation equipment 16,the computing device 26, operators of the computing device 26 or theindustrial automation equipment 16, or the like. For example, thedatabases 44 may include information regarding procedures for operatingand/or maintaining the industrial automation equipment 16. Theprocedures, as such, may include steps to perform, tools to use,personal protective equipment to wear, and the like with regard to theoperations being performed.

The databases 44 may also include information regarding variousregulations related to how the industrial automation equipment 16 shouldbe maintained or operated. Additionally, the regulations may be relatedto how maintenance operations should be documented by the user of thecomputing device 26. The databases 44 may also include data related towarranty information for the industrial automation equipment 16, servicecontact information related to the industrial automation equipment 16,manuals for operating the industrial automation equipment 16, and otherinformation that may be useful to an operator of the industrialautomation equipment 16.

In certain embodiments, the cloud-based computing system 28 may alsoinclude access to various resources 46. The resources 46 may be aresource database or collection of published documents or webpages thatmay be related to the industrial automation equipment 16. As such, theresources 46 may be accessed by the cloud-based computing system 28available via the Internet or other communication networks. Thecloud-based computing system 28 may search or consult the resources 46to acquire data related to the industrial automation equipment 16. Forinstance, the resources 46 may provide information regarding productrecalls or safety concerns related to the industrial automationequipment 16, weather advisory notices for the industrial automationsystem, and the like. Additionally, the resources 46 may includehardware, software or firmware updates, software patches, vulnerabilitypatches, certificates, and the like.

FIG. 5 is a schematic diagram of an industrial automation environment 50including a multi-purpose sensing device 52, in accordance withembodiments presented herein. FIG. 6 is a schematic diagram of examplecomponents of the multi-purpose sensing device of FIG. 5, in accordancewith embodiments presented herein. FIGS. 5 and 6 are discussed togetherbelow for clarity.

As depicted in FIG. 5, the industrial automation environment 50 mayinclude the industrial automation equipment 16 operating in a processarea 54 of a facility. As illustrated, a technician 56 may use thecomputing device 26 to assist in performing a procedure, such as placingthe industrial automation equipment 16 (e.g., machinery) offlineaccording to a corresponding lockout-tagout protocol. The industrialautomation environment 50 is illustrated as including the HMI 12 and thecontrol/monitoring device or automation controller 14 adapted tointerface with other industrial automation equipment, as describedabove.

In addition to the industrial automation equipment described above withrespect to FIG. 1, a multi-purpose sensing device 52 may be coupled tothe industrial automation equipment 16 (e.g., machine, pump). Themulti-purpose sensing device 52 may be located at any suitable positionon the industrial automation equipment 16. In some embodiments, themulti-purpose sensing device 52 may be physically coupled to theindustrial automation equipment 16 using any suitable mechanism (e.g.,bolts, screws, adhesives, magnets). Further, the multi-purpose sensingdevice 52 may be located at other suitable locations in the process area54. For instance, the multi-purpose sensing device 52 may be secured toa ceiling, wall, post, or the like of the process area 54. Themulti-purpose sensing device 52 may be configured to obtain data, readdata, receive data, process data, transmit data, and the like, as willbe further detailed below.

As depicted in FIG. 6, in some embodiments, the multi-purpose sensingdevice 52 may include one or more gas sensors 58, temperature sensors60, motion sensors 62, vibration sensors 64, sound sensors 66,processors 68, memories 70, communication components 72, batteries 74,beacon patches 76, location beacons or tags 78, cameras 79, and thelike. The communication components 72 may be similar to thecommunication component 35 discussed above. As such, the communicationcomponents 72 may include a wireless or wired communication componentthat may facilitate communication with the computing device 26, theindustrial automation equipment 16 (e.g., machine), the cloud-basedcomputing system 28, the control/monitoring device 14, and othercommunication capable devices. In embodiments that use wirelesscommunication, the communication component 72 may include an antenna andenable the processor 68 to send and receive data using any suitablewireless protocol, such as Wi-Fi, Bluetooth®, near field communications,or the like. Further, the antenna 72 may measure the distance betweenother sensors (e.g., multi-purpose sensing devices 52 or individualsensors) associated with the multi-purpose sensing device 52 to assistin determining directions to the physical locations of the sensors,among other things. Also, the antenna 72 may measure and/or calibratethe technician's location in relation to the industrial automationequipment 16. Additionally, the antenna 72 may receive requests from thecomputing device 26, the cloud-based computing system 28, and/or theindustrial automation equipment 16 for on-demand measurements from oneor all of the sensors 58, 60, 62, 64, and 66 and camera 79.

The processor 68 may be similar to the processor 36 described above.That is, the processor 68 of the multi-purpose sensing device 52 may beany type of computer processor or microprocessor capable of executingcomputer-executable code. The processor 68 may also include multipleprocessors that may perform the operations described below. The memory70 may be any suitable articles of manufacture that can serve as mediato store processor-executable code, data, or the like. These articles ofmanufacture may represent computer-readable media (e.g., any suitableform of memory or storage) that may store the processor-executable codeused by the processor 68 to perform the presently disclosed techniques.

Generally, the processor 68 may execute computer-readable code thatdetermines certain properties associated with the industrial automationequipment 16 and/or the environment surrounding the industrialautomation equipment 16 based on the signals received from the sensors.Further, the processor 68 may perform one or more preventative actions(e.g., send notification to computing device 26, cloud-based system 28,the control/monitoring device 14, or some combination thereof, send acommand to the industrial automation equipment 16 and/or thecontrol/monitoring device 14), diagnostics, and/or predictive operationswhen the measurements are outside a range of expected measurements, whenthe measurements vary from baseline signatures (e.g., a baselinecollective measurement signature and/or a baseline individualmeasurement signature), the technician 56 is inside the industrialautomation equipment 16, a certain operational parameter is detected,and the like. In one embodiment, the processor 68 may performtriangulation techniques to assist the computing device 26 indetermining directions to the multi-purpose sensing device 52 and/or theindustrial automation equipment 16 to which the device 52 is attachedbased on at least three known locations of various machines orcomponents in the industrial automation environment 50.

The memory 70 may also be used to store the data, analysis of the data,and the like. Like the memory 37 discussed above, the memory 70 mayrepresent non-transitory computer-readable media (e.g., any suitableform of memory or storage) that may store the processor-executable codeused by the processor 64 to perform various techniques described herein.It should be noted that non-transitory merely indicates that the mediais tangible and not a signal.

In some embodiments, the battery 74 may be rechargeable and may includeany suitable electrode materials and/or electrolytes (e.g., nickelcadmium (NiCd), nickel metal hydride (NiMH), lithium ion (Li-ion),lithium ion polymer (Li-ion polymer)) that enable storing additionalcharges. In other embodiments, the battery 74 may use a single chargeand be replaced with a new battery 74 when the lifetime of the chargeexpires.

The discussion below of the various sensors 58, 60, 62, 64, and 66 andcameras 79 focuses on the sensors and cameras sending data signals tothe processor 68 of the multi-purpose sensing device 52 to performanalysis and one or more preventative actions. As such, the sensors 58,60, 62, 64, and 66 and cameras 79 may be communicatively coupled to theprocessor 68. However, it should be understood that the data signalsfrom the sensors 58, 60, 62, 64, and 66 and cameras 79 may betransmitted via the communication component 72 to the computing device26, the cloud-based computing system 28, and/or the control/monitoringdevice 14 to perform the analysis and/or to perform the one or morepreventative actions, diagnostics, and/or predictive operationsdescribed below.

In some embodiments, the one or more gas sensors 58 may enable measuringoxygen content, carbon dioxide content, moisture content, and the likein the ambient air surrounding the multi-purpose sensing device 52and/or in the environment inside or around the industrial automationequipment 16. The processor 68 may determine a concentration ofpollutants (e.g., parts per million by volume (ppmv)) in the ambient airand/or in gaseous emissions to the ambient air from the industrialautomation equipment 16 based on signals received from the gas sensors58. In addition, the processor 68 may determine if a certain amount offuel/oxygen mixture is detected in the ambient air and perform apreventative action when detected. Further, the processor 68 may performthe preventative action, diagnosis, and/or predictive operation when theoxygen content, carbon dioxide content, moisture content, and/orconcentration of pollutants is outside a range of expected measurementsor when signatures including the content measurement vary from baselinesignatures during certain modes of operation of the industrialautomation equipment 16 (e.g., startup, combustion, idling, shutdown).That is, a baseline collective measurement signature may be determinedbased on historical measurements from multiple sensors (e.g., sensors58, 60, 62, 64, and 66) over different periods of time (e.g., duringdifferent modes of operation of the industrial automation equipment 16)and an individual measurement signature may be determined based onhistorical measurements from a single sensor. When subsequent collectivemeasurements signatures and/or individual measurement signatures varyfrom the respective baselines, the preventative action, diagnostics,and/or predictive operation may be performed.

The preventative action may include sending a command (e.g., power offcommand) to the control/monitoring device 14 and/or the industrialautomation equipment 16, sending an alert to the computing device 26,sending an alert to external systems (e.g., cloud-based computing system28), triggering an alarm in the facility, and so forth. The diagnosticsmay include determining what is causing the variance between signaturesby searching the memory 70 to determine if there is an associated reasonfor the variance at the particular mode or phase of operation of theindustrial automation equipment 16. The predictive operation may includedetermining if the detected signatures indicate a certain part or deviceof the industrial automation equipment 16 or other equipment in thefacility is likely to fail and communicating with or controlling thatpart or device to attempt to inhibit the failure, or sending an alert tothe computing device 26 indicating the prediction.

The temperature sensors 60 may include a thermometer that measures thetemperature of the industrial automation equipment 16 on which themulti-purpose sensing device 52 may be attached or coupled therewith,the temperature of the air surrounding the industrial automationequipment 16, and so forth. Based on the measured temperatures, theprocessor 68 may perform a preventative action, diagnostic, and/orpreventative operation as described above, when the temperatures areoutside a range of expected temperature measurements and/or when thecollective measurement signature and/or individual measurement signaturevaries from the baseline collective measurement signature and/orbaseline individual measurement signature, respectively, during certainmodes of operation of the industrial automation equipment 16.

The motion sensors 62 may include any type of sensor designed to detectmotion of an object in the presence of the sensor. For example, themotion sensors 62 may include a passive infrared (PIR) sensor thatsenses heat movement. For example, the motion sensors 62 may measure aperson's (e.g., technician 56) skin temperature in contrast to otherobjects in the surrounding area to determine whether the technician isnear the motion sensors 62 (e.g., inside or outside of the industrialautomation equipment 16). In some embodiments, one more preventativeaction, as described above, may be performed when the technician 56 isdetected near the motion sensors 62. That is, in some embodiments,detecting movement using the motion sensors 62 may indicate that thetechnician 56 is in a specific area of the industrial environment (e.g.,within the industrial automation equipment 16) and certain options(e.g., various controls of the industrial automation equipment 16,functionalities on software on the computing device 26 that assists thetechnician 56 in performing a lockout-tagout procedure) may not beavailable to the technician 56 or may cease to be available relative todetermining that the technician 56 is not in the specific area.Additionally, certain functionalities may become available when thetechnician 56 is determined to be in certain specific areas.

The vibration sensors 64 may include any suitable vibration sensor, suchas an accelerometer, knock sensor, and the like. The vibration sensors64 may acquire data signals that indicate a frequency of vibration ofthe industrial automation equipment 16 or object attached to thevibration sensors 64. In one embodiment, the data signal acquired by thevibration sensors 64 may be provided to the processor 68. As such, theprocessor 68 may determine one or more operational parameters of theindustrial automation equipment 16. For example, the processor 68 maydetermine whether the industrial automation equipment 16 is on or offand/or whether the industrial automation equipment 16 is operatingnormally or abnormally based on the signals. That is, if the signalsindicate that the industrial automation equipment 16 is vibrating at acertain frequency, the processor 68 may determine that the industrialautomation equipment 16 is operational. In addition, the processor 68may determine the frequency at which the industrial automation equipment16 near optimally runs and/or the frequency at which the industrialautomation equipment 16 runs inefficiently.

Indeed, the processor 68 may learn or determine a range of expectedfrequency measurements as the industrial automation equipment 16operates over time, and, when the measured frequency is outside thedetermined range, the processor 68 may perform a preventative action,diagnostic, and/or predictive operation, as discussed above. Toillustrate, a frequency with an amplitude above the upper limit of therange may indicate that the industrial automation equipment 16 isoperating abnormally because of a damaged part or the like. Also, theprocessor 68 may perform a preventative action, diagnostic, and/orpredictive operation, when the collective measurement signature and/orindividual measurement signature varies from the baseline collectivemeasurement signature and/or baseline individual measurement signaturefor the vibration sensors 64, respectively, during certain modes ofoperation of the industrial automation equipment 16. Additionally, thesound sensors 66 may also assist the processor 68 in determiningoperational parameters of the industrial automation equipment 16. Thesound sensors 66 may include any type of sensor that is capable ofdetecting sound waves generated in the presence of the sensor. Forexample, in some embodiments, the sound sensors 66 may include amicrophone that measures sounds of the industrial automation equipment16 or sounds in the environment surrounding the industrial automationequipment 16. Based on the measured sound data signals from the soundsensors 66, the processor 68 may determine when the industrialautomation equipment 16 is in a particular operating state (e.g., on,off) and/or a speed of operation. In addition, the processor 68 maylearn or determine a range of expected audio measurements for particularstates of the industrial automation equipment 16 (e.g., normaloperation, abnormal operation) as the industrial automation equipment 16operates over time, and the processor 68 may determine whether theindustrial automation equipment 16 is operating normally or not based onwhether received audio data is outside of the ranges. Further, abaseline collective measurement signature may be determined based onhistorical measurements from the sensors 58, 60, 62, 64, and 66 overdifferent periods of time (e.g., modes of operation of the industrialautomation equipment 16) and an individual collective measurementsignature may be determined based on historical measurements from onesensor, such as the sound sensors 66. When subsequent collectivemeasurement signatures and/or individual measurement signatures varyfrom the baselines, the preventative action, diagnostics, and/orpredictive operation may be performed.

To illustrate, in one example, the multi-purpose sensing device 52 mayinclude any suitable number (e.g., 17) of sensors and may be attached tothe industrial automation equipment 16, such as a printing press. The 17sensors may be networked together and provide measurements to theprocessor 68 that can adjust the operation of the printing press, aprocess including the printing press, safety protocols, and so forthdepending on how many sensors are used, where people are located in thefacility, and what the people are doing. In this way, multi-dimensionalmeasurements (e.g., collective measurement signatures) may be obtainedfrom the 17 sensors disposed within the multi-purpose sensing device 52that is coupled to the printing press (e.g., single machine) and may beused by the processor 68 to at least enhance the operation of theprinting press or any suitable industrial automation equipment 16.

In some embodiments, the beacon patch 76 may provide additional data tothe computing device 26 and/or the cloud-based computing system 28related to a distance between neighboring devices to the multi-purposesensing device 52 and/or the industrial automation equipment 16 to whichthe multi-purpose sensing device 52 is attached. The beacon patch 76 mayinclude electronic circuitry that is capable of broadcasting a signalthat may be received by other electronic devices (e.g., computing device26). The signal may include information such as the proximity of thebeacon patch 76 to other devices (e.g., computing device 26). In someembodiments, the beacon patch 76 may use near field communicationtechnology (e.g., Bluetooth®), which may include low energy proximitysensing technology or the like, to determine the proximity of thedevices near the beacon patch 76. In some embodiments, the distancesbetween devices may assist in determining directions from the computingdevice 26 to the multi-purpose sensing device 52 by enabling determiningthe location of the devices.

To that end, in some embodiments, the location beacon or tag 78 may beused to enable the computing device 26 and/or the cloud-based computingsystem 28 to determine directions for the technician 56 to reach themulti-purpose sensor 52 and/or the respective industrial automationequipment 16 associated with the multi-purpose sensing device 52. Thelocation beacon or tag 78 may include electronic circuitry that iscapable of broadcasting a signal that may be received by otherelectronic devices (e.g., computing device 26). In some embodiments, thelocation beacon or tag 78 may use Bluetooth® Low Energy (BLE) thatenables broadcasting the signal electronic devices within a certainproximity. In some embodiments, the location beacon or tag 78 may emitcoordinates to its location, the location of the multi-purpose sensingdevice 52, and/or the location of the industrial automation equipment16. For example, the location beacon or tag 78 may emit a uniqueidentifier that may be received by certain software operating on thecomputing device 26. The unique identifier may be used by the softwareto determine the multi-purpose sensing device's physical location. Inone embodiment, the processor 68 may use triangulation schemes to assistthe computing device 26 in determining directions to the multi-purposesensing device 52 or the industrial automation equipment 16.Triangulation schemes may include determining the location of themulti-purpose sensing device 52 or the industrial automation equipment16 by measuring the angles to the multi-purpose sensing device 52 or theindustrial automation equipment 16 from known points at the ends of afixed baseline (e.g., a wall of a facility, a line between two otherknown points (other industrial automation equipment in the facility)).For example, the distances between the beacon patch 76 and the computingdevice 26 received from the beacon patch 76 may be used in conjunctionwith the physical location of the multi-purpose sensing device 52received location beacon or tag 78 when determining the directions tothe multi-purpose sensing device 52. In some embodiments, emitters maybe strategically placed in the facility for the purpose of finding thelocation of the multi-purpose sensing device 52 or the industrialautomation equipment 16 by broadcasting the location of the emitters fordevices (e.g., computing device 26) to read. The location of themulti-purpose sensing device 52 or the industrial automation equipment16 can be fixed as the third point of a triangle based on knownlocations, such as the locations of the emitters. The processor 68 maythen determine a route to the location of the multi-purpose sensingdevice 52 or the industrial automation equipment 16 based on thelocation of the computing device 26 being held by the technician 56, thelocation of the technician 56 in the facility, the location of anentrance to the facility, or the like.

The cameras 79 may obtain video and/or image data and send the data tothe processor 68, which may process the data and/or store the data inthe memory 70. In some embodiments, the video and/or image data may beobtained by the camera 79 within a visible light range or outside thevisible light range, within an infrared light range, and/or within anultraviolet light range. In some embodiments, the processor 68 mayrecord positions of certain objects in the received video and/or imagedata as baseline positions when the industrial automation equipment 16is operating normally. For example, tags and other objects may beassociated with and located at various positions on the industrialautomation equipment 16. Upon receiving video and/or image dataassociated with the same industrial automation equipment 16 that haspreviously been viewed, the processor 68 may determine the positions ofthe objects in relation to the baseline positions to determine whetherthe objects have moved. In other words, historical video data may becompared to current video data to determine whether positions of objectshave changed. In some embodiments, object recognition techniques may beused by the processor 68 to determine whether positions of objects havechanged. For example, a piece of industrial automation equipment 16 mayhave a physical tag positioned on a door handle according to baselinevideo or image data and the tag may be positioned on the top of thepiece of industrial automation equipment 16 in other video or imagedata. Here, the processor 68 may determine that the tag has been movedand perform some action (e.g., send an alert to the display 42) toindicate the change. The change may, for example, result from a doorbeing opened or excess vibration. Further, in some embodiments, thevideo and/or image data may be analyzed by the processor 68 to determinewhether any hazards are located within a certain proximity to theindustrial automation equipment 16, and the processor 68 may performsome actions when a hazard is detected, as described in detail belowwith respect to FIGS. 12 and 13.

FIG. 7 is a flow diagram of a method 80 for performing a preventativeaction when measurements are outside a range of expected measurements,in accordance with embodiments presented herein. Although the followingdescription of the method 80 is described with reference to theprocessor 68 of the multi-purpose sensing device 52, it should be notedthat the method 80 may be performed by other processors disposed onother devices that may be capable of communicating with themulti-purpose sensing device 52, such as the computing device 26, thecloud-based computing system 28, the control/monitoring device 14, theindustrial automation equipment 16, or other components associated withthe industrial application 24. Additionally, although the followingmethod 80 describes a number of operations that may be performed, itshould be noted that the method 80 may be performed in a variety ofsuitable orders and all of the operations may not be performed. Itshould be appreciated that the method 80 may be wholly executed by themulti-purpose sensing device 52 or the execution may be distributedbetween the computing device 26, the control/monitoring device 14 and/orthe cloud-based computing system 28.

Referring now to the method 80, the processor 68 may receive (block 82)data signals (e.g., first sets of data) from the one or more gas sensors58, temperature sensors 60, motion sensors 62, vibration sensors 64,and/or sound sensors 66 over time as the industrial automation equipment16 operates. The data signals may each include a set of data indicatingmeasurements or readings taken by the sensors. The processor 68 may alsodetermine (block 84) ranges for expected measurements for each of thesets of data associated with the respective sensors 58, 60, 62, 64, and66. For example, the processor 68 may continuously monitor the receiveddata signals from each of the sensors 58, 60, 62, 64, and 66 to learn ordetermine a range for expected measurements. In some embodiments, theprocessor 68 may monitor the data signals for a threshold period of time(e.g., 10 minutes, 30 minutes, 60 minutes) to determine the range ofexpected measurements. In other embodiments, the processor 68 maymonitor the data signals until a threshold number of readings (e.g., 5,10, 15, 20) are received that indicate measurements within a thresholddifferential to each other to determine the range of expectedmeasurements. In yet other embodiments, the processor 68 may obtain therange of expected measurements for each of the sensors 58, 60, 62, 64,and 66 from the memory 70, the computing device 26, and/or thecloud-based computing system 28, such that the range of expectedmeasurements may be determined based on data related to other similardevices.

Further, the processor 68 may determine (block 84) baseline individualsensor measurement signatures for various periods of time. The periodsof time may correspond to different modes of operation of the industrialautomation equipment 16 (e.g., startup, slow-down, combustion, shutdown). For example, the signature for vibration sensor measurements mayindicate that during startup the amplitude of the frequency increasesand begins to decrease as the industrial automation equipment 16 beginsto operate at a steady state. It should be understood that the baselineindividual sensor measurement signatures may be obtained formeasurements for each sensor 58, 60, 62, 64, and 66 and may be based onhistorical sensor measurements.

The processor 68 may also determine (block 86) whether received datasignals indicate measurements that are outside the respective range ofexpected measurements and/or vary from the baseline individual sensormeasurement signatures. In some embodiments, the received data signals(e.g., second sets of data) may be received subsequent to the processordetermining the ranges of expected measurements and the baselineindividual sensor measurement signatures. For example, if sensormeasurements from the vibration sensor indicate a lower aggregateamplitude during startup than the baseline individual sensor measurementsignature for the vibration sensor 64, then the processor 68 maydetermine that the industrial automation equipment 16 is operatingabnormally.

Additionally, the processor 68 may perform (block 88) one or morepreventative actions, diagnostics, and/or predictive operations when themeasurements are outside a range of expected measurements and/or varyfrom the baseline individual sensor measurement signatures. In someinstances, the preventative action may vary based on whether themeasurements are below or above the range of expected measurements, andmay vary by the size of the differential between the measurement and therange. As previously discussed, in some embodiments, the preventativeactions may include sending an alert to the computing device 26, thecontrol/monitoring device 14, and/or cloud-based computing system 28,sending a command (e.g., power off command) to the industrial automationequipment 16, triggering an alarm in the facility, and so forth. In someembodiments, the diagnostics may include determining what is causing thediscrepancy between the subsequent measurements and the baselineindividual sensor measurement signatures by searching the memory 70 todetermine if there is an associated reason for the variance betweensignatures. The predictive operation may include determining if thedetected signatures indicate a certain part or device is likely to failbased on the historical data associated with the detected signatures andcommunicating with or controlling that part or device to attempt toinhibit the failure. That is, in certain embodiments, certain detectedsignatures that are different from a respective baseline signature maybe associated with a particular condition or result. As such, thepredictive operation may be determined based on the condition or resultthat is associated with the detected signature.

To illustrate, if a measurement by any of the sensors 58, 60, 62, 64,and 66 of the multi-purpose sensing device 52 is outside a range ofexpected measurements, then the processor 68 may send a command to thecontrol/monitoring device 14 to command the control/monitoring device 14and/or the industrial automation equipment 16 to go offline. Theprocessor 68 may also send an alert to the computing device 26indicative of the measurement being outside the range. In someembodiments, the computing device 26 may display the alert and/ordisable certain functionality presented by software used to assist thetechnician in performing procedures, such as placing the industrialautomation equipment 16 offline using the lockout-tagout protocol, basedon the alert received from the multi-purpose sensing device 52. Forexample, any functionality of the software that enables controlling theindustrial automation equipment 16 may be disabled (e.g., graphicalelements grayed out, inoperable, or removed) to inhibit the technician56 from turning the industrial automation equipment 16 on when it isundesirable due to the non-conforming measurements.

Additionally, when comparing collective measurement signatures, amulti-dimensional baseline of sensor measurements over a time period(e.g., baseline collective measurement signature) may be compared torespective subsequent sensor measurements received by the sensors 60,62, 64, and 66 at a later time over similar time period duration. Thatis, the baseline collective measurement signature may detail theexpected sensor measurements for multiple sensors over some period oftime. Subsequent sensor measurements acquired by the multiple sensorsmay then be organized as a collective measurement signature and comparedto the baseline collective measurement to ensure that the industrialautomation equipment 16 or some other monitored device is operatingproperly. In some instances, one sensor measurement signature acquiredfrom a particular sensor may vary from a respective baseline sensormeasurement signature expected for the same sensor. However, whenevaluating the one sensor measurement signature with respect to amulti-dimensional baseline of sensor measurements (e.g., baselinecollective measurement signature), it may be determined that whenevaluating the one sensor measurement with respect to each of the othersensor measurements that make up the baseline collective measurementsignature, the one sensor measurement may substantially match therespective sensor measurement signature in the baseline collectivemeasurement signature during a particular mode of operation of theequipment 16. As such, the processor 68 may determine that the equipment16 is operating normally for that particular mode of operation. In otherwords, if an acquired audio measurement signature varies from an audiomeasurement signature of the baseline measurement signature for theaudio sensor, but the acquired audio sensor measurement signature whenviewed with a vibration sensor measurement signature substantiallymatches the baseline collective measurement signature having both audioand vibration sensor measurements for some mode of operation of theequipment 16, then the processor 68 may determine that the equipment 16is operating normally for that mode of operation.

FIG. 8 is a flow diagram of a method 90 for determining instructions toa physical location of the multi-purpose sensing device 52 and/or anindustrial automation equipment 16 to which the multi-purpose sensingdevice is attached, in accordance with embodiments presented herein.Although the following description of the method 90 is described withreference to the processor 36 of the computing device 26, it should benoted that the method 90 may be performed by other processors disposedon other devices that may be capable of communicating with themulti-purpose sensing device 52, such as the cloud-based computingsystem 28, the control/monitoring device 14, the industrial automationequipment 16, or other components associated with the industrialapplication 24. Additionally, although the following method 90 describesa number of operations that may be performed, it should be noted thatthe method 90 may be performed in a variety of suitable orders and allof the operations may not be performed. It should be appreciated thatthe method 90 may be wholly executed by the computing device 26 or theexecution may be distributed between the multi-purpose sensing device 52and/or the cloud-based computing system 28.

Referring now to the method 90, the processor 36 may receive (block 92)location data from the multi-purpose sensing device 52. In someembodiments, the location data may include coordinates (e.g., globalpositioning system coordinates) of the location beacon or tag 78representing a point at which the location beacon or tag 78 is locatedin the facility. Additionally, the location data may also includeinformation from the beacon patch 76 related to neighboring devices,such as the proximity of the neighboring devices and/or the locations ofthe neighboring devices. In some embodiments, the computing device 26may use the location data to determine (block 94) directions to themulti-purpose sensing device 52 and/or the industrial automationequipment 16 to which the multi-purpose sensing device 52 is attached.Additionally, the processor 68 of the multi-purpose sensing device 52may process the signals from the location beacon or tag 68 usingtriangulation techniques to assist the computing device 26 indetermining directions to the multi-purpose sensing device 52. Theprocessor 36 of the computing device 26 may also cause the directions todisplay (block 96) on the display 42 so the technician 56 holding thecomputing device 26 may navigate through the facility to themulti-purpose sensing device 52 and/or the industrial automationequipment 16. This may be particularly useful for efficiently findingparticular industrial automation equipment 16 associated with steps ofthe lockout-tagout protocol provided by the software on the computingdevice 26.

FIG. 9 is a flow diagram of a method 100 for performing a preventativeaction based on the location of a technician 56, in accordance withembodiments presented herein. Although the following description of themethod 100 is described with reference to the processor 68 of themulti-purpose sensing device 52, it should be noted that the method 80may be performed by other processors disposed on other devices that maybe capable of communicating with the multi-purpose sensing device 52,such as the computing device 26, the cloud-based computing system 28,the control/monitoring device 14, the industrial automation equipment16, or other components associated with the industrial application 24.Additionally, although the following method 100 describes a number ofoperations that may be performed, it should be noted that the method 100may be performed in a variety of suitable orders and all of theoperations may not be performed. It should be appreciated that themethod 100 may be wholly executed by the multi-purpose sensing device 52or the execution may be distributed between the computing device 26and/or the cloud-based computing system 28.

Referring now to the method 100, the processor 68 may receive (block102) data signals from the sensors (e.g., motion sensors 62) indicatingthe technician's location. Using the data signals, the processor 68 maydetermine (block 104) whether the technician 56 is inside or outside ofsome industrial automation equipment 16. The processor 68 may perform(block 106) one or more preventative actions when the technician 56 isdetected as being within a predetermined range of the industrialautomation equipment 16, a threshold proximity to the industrialautomation equipment 16, or the like. That is, in some embodiments,detecting movement using the motion sensors 62 may indicate that thetechnician 56 is in a specific area of the industrial environment (e.g.,within the industrial automation equipment 16) and certain options maynot be available to the technician 56 or may cease to be availablerelative to determining that the technician 56 is outside the specificarea. For example, the processor 68 may cause the communicationcomponent 72 to send a command to the automation controller 14 to turnoff power to the industrial automation equipment 16 and send an alert tothe computing device 26, which may be displayed by the computing device26 and/or cause industrial automation equipment 16 control functionalityto be disabled from the software used to assist the technician 56 inplacing the industrial automation equipment 16 offline according to thelockout-tagout procedure.

FIG. 10 is a flow diagram of a method 110 for performing a preventativeaction based on operational parameters, in accordance with embodimentspresented herein. Although the following description of the method 110is described with reference to the processor 68 of the multi-purposesensing device 52, it should be noted that the method 110 may beperformed by other processors disposed on other devices that may becapable of communicating with the multi-purpose sensing device 52, suchas the computing device 26, the cloud-based computing system 28, thecontrol/monitoring device 14, the industrial automation equipment 16, orother components associated with the industrial application 24.Additionally, although the following method 110 describes a number ofoperations that may be performed, it should be noted that the method 110may be performed in a variety of suitable orders and all of theoperations may not be performed. It should be appreciated that themethod 110 may be wholly executed by the multi-purpose sensing device 52or the execution may be distributed between the computing device 26and/or the cloud-based computing system 28.

Referring now to the method 110, the processor 68 may receive (block112) audio signals from one or more sensors (e.g., sound sensors 66,vibration sensors 64 (knock sensor)). Based on the audio signals, theprocessor 68 may determine (block 114) various operational parameters ofthe industrial automation equipment 16. The operational parameters mayinclude whether the industrial automation equipment 16 is operatingnormally or abnormally by comparing the frequencies of the audio signalsto a range of expected frequencies, to frequency signatures stored inthe memory 70 that indicate normal and abnormal operation, and so forth.Further, the operational parameters may be determined by obtaining abaseline collective measurement signature including the audiomeasurements over time and/or a baseline individual sensor measurementsignature for the audio measurements over time and comparing subsequentaudio measurement signatures at the same point in time to the respectivebaselines. The processor 68 may also perform (block 116) one or morepreventative actions, diagnostics, and/or predictive operations asdescribed above, based on the operational parameters.

FIG. 11 is a flow diagram of a method 120 for comparing signatures basedon sensor measurements for various periods of time, in accordance withembodiments presented herein. Although the following description of themethod 120 is described with reference to the processor 68 of themulti-purpose sensing device 52, it should be noted that the method 120may be performed by other processors disposed on other devices that maybe capable of communicating with the multi-purpose sensing device 52,such as the computing device 26, the cloud-based computing system 28,the control/monitoring device 14, the industrial automation equipment16, or other components associated with the industrial application 24.Additionally, although the following method 120 describes a number ofoperations that may be performed, it should be noted that the method 120may be performed in a variety of suitable orders and all of theoperations may not be performed. It should be appreciated that themethod 120 may be wholly executed by the multi-purpose sensing device 52or the execution may be distributed between the computing device 26, thecontrol/monitoring device 14, and/or the cloud-based computing system28.

Referring now to the method 120, the processor 68 may receive (block122) a first set of sensor measurements over time. That is, the firstset of sensor measurements may include historical data regarding variousproperties associated with the industrial automation equipment 16 overtime to enable the processor to determine (block 124) a baselinecollective measurement signature for the different periods of time(e.g., operations of the industrial automation equipment 16) using thehistorical first set of sensor measurements. The processor 68 may thenreceive (block 126) a second set of sensor measurements for a particulartime of operation of the industrial automation equipment 16. Further,the processor 68 may determine (block 128) a subsequent collectivemeasurement signature for the second set of sensor measurements at theparticular time of measurement (e.g., during a phase or mode ofoperation of the industrial automation equipment 16).

Then, the processor 68 may determine (block 130) whether themulti-dimensional sensor measurements of the subsequent collectivemeasurement signature of the second set of sensor measurements variesfrom the multi-dimensional measurements of the baseline collectivemeasurement signature for some particular time of measurement (e.g.,phase or mode of operation of the industrial automation equipment 16).That is, the baseline collective measurement signature may enable theprocessor 68 to determine whether industrial automation equipment 16 isoperating normally or abnormally by comparing each of the respectivesensor measurement signatures in the subsequent collective measurementsignature relative to the sensor measurement signatures of the baselinecollection of measurements during various periods of times (e.g., modesor phases of operation of the industrial automation equipment 16). Forexample, if a subsequent audio signature is determined to vary from theaudio signature of the baseline measurement signature for the audiosensors, but the combination of the subsequent audio signature and thesubsequent vibration signature substantially matches the signatures ofthe baseline collective measurement signature during start-up of theindustrial automation equipment 16, then the processor 68 may determinethat the industrial automation equipment 16 is operating normally atstartup.

When the signatures of the subsequent collective measurement signaturedo not vary from the signatures of the baseline collective measurementsignature, then the processor 68 may determine (block 124) the baselinecollective measurement signature again using the subsequent collectivemeasurement to maintain a current baseline collective measurementsignature. In contrast, when the measurement signatures of thesubsequent collective measurement signature vary from the measurementsignatures of the baseline collective measurement signature, theprocessor 68 may perform (block 132) a preventative action, diagnostic,and/or predictive operation, as described above. In some embodiments,the determination that the measurements or signatures vary betweencollective measurement signatures may be made when more than a thresholdnumber (e.g., 2, 3, 4) of respective individual sensor measurements orsignatures of the collective measurement signatures vary, when one ofthe individual sensor measurement or signature between the collectivemeasurement signatures varies by a threshold amount (e.g., a largevariance versus a small variance), or both.

Further, in some embodiments, the processor 68 may learn that somesensor measurement signature variances are indicative of a failure,fault, or abnormal behavior of the industrial automation equipment 16.For example, in some instances, as discussed above, the processor 68 maydetermine that the industrial automation equipment 16 is operatingnormally for the particular mode of operation when a sensor measurementsignature of the subsequent collective measurement signature varies fromthe respective sensor measurement signature of the baseline collectivemeasurement signature because other sensor measurement signatures do notsubstantially vary. Thus, the processor 68 may not take any action.However, if a fault, failure, or abnormal behavior occurs, then thechange in the sensor measurement signature may be logged in the memory70 (e.g., by a user or automatically via the processor 68) and a reasonfor the fault, failure, or abnormal behavior may be associated with thatvariance at the particular mode of operation of the industrialautomation equipment 16. In this way, the processor 68 may continuouslylearn measurement signature variances that are indicative of faults,failures, or abnormal behavior to enable better decision makingregarding which preventative action to take, better diagnostics, and/orbetter predictive operations scheduling.

To illustrate, if a different device connected to the industrialautomation equipment 16 fails after the variance is detected, thevariance including a reason and/or potential effects of the variance maybe logged for the particular mode of operation of the industrialautomation equipment 16. Then, when the variance is detected in thefuture, the processor 68 may predict that the device will fail and maysend the computing device 26 a notice of the prediction. Further, if avariance is detected and the processor 68 does not take any action andthen the industrial automation equipment 16 fails. A technician mayservice the equipment to determine what caused the failure and can logthe reason for the variance into the multi-purpose sensing device 52using the computing device 26, thereby enabling the processor 68 todiagnose what the cause of the variance is in the future if detectedagain.

FIG. 12 is a flow diagram of a method 140 for operating the industrialautomation equipment 16 efficiently and/or productively based on sensordata and/or image/video data, in accordance with embodiments presentedherein. Although the following description of the method 140 isdescribed with reference to the processor 68 of the multi-purposesensing device 52, it should be noted that the method 140 may beperformed by other processors disposed on other devices that may becapable of communicating with the multi-purpose sensing device 52, suchas the computing device 26, the cloud-based computing system 28, thecontrol/monitoring device 14, the industrial automation equipment 16, orother components associated with the industrial application 24.Additionally, although the following method 140 describes a number ofoperations that may be performed, it should be noted that the method 140may be performed in a variety of suitable orders and all of theoperations may not be performed. It should be appreciated that themethod 140 may be wholly executed by the multi-purpose sensing device 52or the execution may be distributed between the computing device 26and/or the cloud-based computing system 28.

Referring now to the method 140, the processor 68 may receive (block142) data signals from the one or more gas sensors 58, temperaturesensors 60, motion sensors 62, vibration sensors 64, sound sensors 66,and/or camera 79 over time as the industrial automation equipment 16operates. The data signals may each include a set of data indicatingmeasurements or readings taken by the sensors and/or video data obtainedby the camera 79. The received data may be used by the processor 68 tomonitor (block 144) a first set of data including settings of theindustrial automation equipment 16, operational parameters of theindustrial automation equipment 16, signatures from sensor measurements,and/or image/video data when the industrial automation equipment 16 isoperating efficiently and/or productively. The processor 68 maydetermine that the industrial automation equipment 16 is operatingefficiently and/or productively when a rate of output is at a desiredlevel, a certain level of output is attained, or the like. For example,the processor 68 may make this determination when the industrialautomation equipment 16 is producing a desired quantity of products witha certain level of quality.

The settings of the industrial automation equipment may include thespeed at which the industrial automation equipment 16 is operating, thetemperature of the industrial automation equipment 16, and/or anyconfiguration (e.g., operational parameters) that is set to operateindustrial automation equipment 16 in its current state. The signaturesmay include individual signatures of each respective sensor and/or acollective signature of historical data from more than one sensor. Thevideo data may be used to identify positions of components of theindustrial automation equipment 16 when the industrial automationequipment 16 is operating efficiently and/or productively. The processor68 may designate (block 146) the first set of data (e.g., monitoredsettings, operational parameters, and/or signatures when the industrialautomation equipment 16 is operating efficiently and/or productively) asbaseline data.

After determining the baseline data, the processor 68 may monitor (block148) a second set of data including the settings of the industrialautomation equipment 16, operational parameters, signatures, and/orimage/video data to ensure that the industrial automation equipment 16operates efficiently and/or productively. The processor 68 may determine(block 150) whether the signatures and/or image/video data of the secondset of data are different than the signatures and/or image/video data ofthe baseline data. When the signatures and/or image/video data are notdifferent between the second set of data and the baseline data, theprocessor 68 may return to monitoring (block 148) the settings,operational parameters, signatures, and/or image/video data. When thesignatures and/or image video data of the second set of data differ fromthe signatures and/or image/video data of the baseline data, theprocessor 68 may modify (block 152) the settings, the operationalparameters, or both to cause the signatures and/or image/video data toconverge as desired.

For example, the processor 68 may modify the settings and/or operationalparameters of the industrial automation equipment 16 to generate thedesired signatures that indicate the industrial automation equipment 16is operating efficiently and/or productively. Further, the processor 68may compare the image/video data of the second set of data with theimage/video data of the baseline data to determine whether thecomponents of the industrial automation equipment 16 are in the properpositions to enable the industrial automation equipment 16 to operateefficiently and/or productively. After the modifications to thesettings, the operational parameters, and/or positions of controllablecomponents are made, the processor 68 may continuously monitor (block148) the signatures of the sensor data and/or image/video data todetermine (block 150) if the signatures and/or image/video data deviatefrom the signatures and/or image/video data that indicate efficientand/or productive operation. The processor 68 may continue to modify(block 152) the settings, operational parameters, and/or positions ofcontrollable components until the signatures and/or image/video data areacceptable. In this way, the settings and/or operational parametersassociated with the data acquired by the sensors may be used to ensurethat the operation of the industrial automation equipment 16 isenhanced. Additional details with regard to evaluating the baselineimage/video data will now be discussed below.

FIG. 13 is a flow diagram of a method 154 for comparing positions ofobjects based on image/video data for various periods of time, inaccordance with embodiments presented herein. Although the followingdescription of the method 154 is described with reference to theprocessor 36 of the computing device 26, it should be noted that themethod 154 may be performed by other processors disposed on otherdevices such as the multi-purpose sensing device 52, the cloud-basedcomputing system 28, the control/monitoring device 14, the industrialautomation equipment 16, or other components associated with theindustrial application 24. Additionally, although the following method154 describes a number of operations that may be performed, it should benoted that the method 154 may be performed in a variety of suitableorders and all of the operations may not be performed. It should beappreciated that the method 154 may be wholly executed by the computingdevice 26 or the execution may be distributed between the multi-purposesensing device 52, the computing device 26, the control/monitoringdevice 14, and/or the cloud-based computing system 28.

Referring now to the method 154, the processor 36 may receive (block156) baseline image/video data from the camera 79 of the multi-purposesensing device 52. The image/video data may be still images (e.g.,pictures) from specific points in time (e.g., when the industrialautomation equipment 16 is operating normally, efficiently, orabnormally) or may be a continuous video feed taken over time. Forexample, the baseline image/video data may be recorded when theindustrial automation equipment 16 is initially started and operatingefficiently. In some embodiments, the baseline image/video data mayinclude an image of the industrial automation equipment 16 and an imageof an environment surrounding the industrial automation equipment 16. Insome embodiments, a user may designate image/video data as baselinedata. For example, the user may observe and verify the proper placementof components of the industrial automation equipment 16 in certainimage/video data and designate that image/video data as baselineimage/video data. The processor 36 may record (block 158) positions ofobjects in the baseline image/video data. In some embodiments, objectrecognition techniques may be used by the processor 36 to identify theobjects in the image/video data. This may include detection ofpre-defined shape features (and orientations of such shape features)that are disposed on components to facilitate identification andpositional analysis. For example, certain physical tags may includepre-defined shape features (e.g. shape, size, texture) that enableidentification of the components to which the physical tag is attachedand determination of whether the component moves positions. Then, theprocessor 36 may receive (block 160) image/video data from themulti-purpose sensing device 52 that is recorded subsequent to theinitial image/video data. The processor 36 may determine (block 162)whether any objects (e.g., components of the industrial automationequipment 16 or objects around the industrial automation device 16 inthe process area 54) have substantially moved. For example, a piece ofindustrial automation equipment 16 may have a physical tag positioned ona door handle in the baseline image/video data and the tag may bepositioned on the top of the industrial automation equipment 16 in thesubsequent image/video data. In this example, the processor 36 maydetermine that the tag has substantially moved as a result of the doorbeing opened or excess vibration and take one or more actions, asdescribed below. Another example of a tag moving positions between thebaseline image/video data and the subsequent image/video data isdescribed below. The processor 36 may determine (block 164) whether anyhazards exist within a certain proximity to the equipment and performing(block 166) an action, diagnostic, and/or predictive operation when anobject has substantially moved and/or a hazard exists, as described indetail below.

FIG. 14 is a screen capture of a display 168 showing baselineimage/video data 170, in accordance with embodiments presented herein.FIG. 15 is a screen capture of the display showing subsequentimage/video data 172, in accordance with embodiments presented herein.For purposes of clarity, the FIGS. 14 and 15 are discussed together. Tosimplify the following discussion, the display 168 will be described inrelation to the computing device 26.

In the depicted embodiment, the display 168 depicts a portion of thefacility as real objects, which in the depicted embodiment includes acontrol unit real object 174, a motor real object 176, and a conveyorreal object 178. The real objects may visually represent physicalfeatures of the respective industrial automation equipment 16. Forexample, the control unit real object 174, the motor real object 176,and the conveyor real object 178 may be image/video data capturing acontrol unit industrial automation equipment 16, a motor industrialautomation equipment 16, and a conveyer industrial automation equipment16, respectively. As discussed above, the baseline image/video data 170and the subsequent image/video data 172 may be captured via the camera79 of the multi-purpose sensing device 52.

As shown in FIG. 14, a tag real object 180 is attached to the controlunit real object 174 in the baseline image/video data 170. The tag realobject 180 may represent a physical tag that is used to provide productinformation, operating instructions, navigational instructions, or thelike. As shown in FIG. 15, the tag real object 180 is no longer attachedto the control unit real object 174 in the subsequent image/video data172. In this example, the processor 36 may determine that the tag hasfallen off (e.g., substantially moved) of the control unit as a resultof excess vibration, tampering, or the like, and take one or moreactions, as described below. The physical tag may have a specializedshape to enable faster identification and determination of whether thetag has substantially moved.

Returning to the method 154 of FIG. 13, the processor 36 may determine(block 164) whether any hazards exist within a certain proximity to theindustrial automation equipment 16 based on the image/video data. Theproximity may include the physical space presented in the image/videodata 172. Further, the proximity may be configurable in that theprocessor 36 can choose a portion of the image/video data to analyze.For example, the processor 36 may search for any hazard within theprocess area 54. Hazards may include any potential concern, such asanything that affects the ability of the industrial automation equipment16 to operate. Hazards may include water puddles or leaks, debris, loosefacility infrastructure, unknown objects, unauthorized personnel, or thelike. To illustrate, the processor 36 may obtain baseline image/videodata 170 of the industrial automation equipment 16 in normal operatingconditions and then receive subsequent image/video data 172 that theprocessor 36 determines is indicative of water leaking at a locationwithin a close proximity to the industrial automation equipment 16 bycomparing the baseline image/video data 170 and the subsequentimage/video data 172. In some embodiments, the image/video data 170, 172may include certain visual properties that aid the detection of hazards.For example, the image/video data 170, 172 may include thermalsignatures of objects in the physical space presented in the image/videodata 170, 172. The processor 36 may analyze the image/video data 170,172and determine that the thermal signatures exceed a threshold. As aresult, the processor 36 may determine that a hazard is present.

When an object has substantially moved and/or when a hazard is detectedin the subsequent image/video data 172, the processor 36 may perform(block 166) certain actions, a diagnostic, and/or a predictiveoperation. For instance, the actions may include displaying an alert onthe display 42, sending an alert to the cloud-based computing system 28,sounding an alarm in the facility, placing the industrial automationequipment 16 offline, and/or the like. The diagnostic may includeanalyzing the image/video data to determine the cause of the movementand/or hazard. The predictive operation may include determining when themovement and/or hazard may occur in the future based on similarimage/video data. For example, when an object has substantially moved,the processor 36 may generate and send a command signal to theindustrial automation equipment 16 to power down. Further, theimage/video data may be analyzed in conjunction with the data signatures(e.g., individual measurement signatures and/or collective measurementsignature) determined based on the sensor measurements and sent from themulti-purpose sensing device 52. For example, when an object hassubstantially moved positions, the processor 36 may determine that thevibration signature of the industrial automation equipment 16 during thesame timeframe is increased relative to the vibration signature duringnormal operation. As a result, the processor 36 may determine that thecause of the object movement was excess vibration.

It should be noted that the methods 80, 100, 110, 120, and 140 areperformed using the processor 68 that is part of the multi-purposesensing device 52 and the methods 90 and 154 are performed by theprocessor 36 of the computing device 26. However, as discussed above,the methods 80, 100, 110, 120, and 140 may be performed by processorsdisposed on electronic devices in communication with the multi-purposesensing device 52, such as the computing device 26, the cloud-basedcomputing system 28, the control/monitoring device 14, or the like.Also, as discussed above, the methods 90 and 154 may be performed by theprocessor 68 of the multi-purpose sensing deice 52. The multi-purposesensing device 52 and the computing device 26 are configured to executeinstructions that enable each device to interact with the industrialautomation equipment 16. As such, the multi-purpose sensing device 52and the computing device 26 are tied to particular machines to assist inthe management and operations of the industrial automation equipment 16,and thus, the industrial application 24. Moreover, it should be notedthat the data received by the multi-purpose sensing device 52, thecomputing device 26, the cloud-based computing system 28, the industrialautomation equipment 16, or the control/monitoring device 14 may betransformed when being transmitted, analyzed, or depicted for view by auser of the respective device. For example, the alert generated based onsignals received from the sensors 58, 60, 62, 64, and 66 and/orimage/video data received from camera 79 includes a transformation ofthe sensor data signals and/or image/video data to the alert. Also, thedirections generated based on the location data from the multi-purposesensing device 52 include a transformation of the location data signalsto navigational directions. Further, the command signal generated basedon data signals received from the sensors 58, 60, 62, 64, and 66 and/orimage/video data received from the camera 79 enables controlling theindustrial automation equipment 16.

Technical effects of the embodiments described herein include using amulti-purpose sensing device 52 that may include a number of varioussensors 58, 60, 62, 64, and 66, a beacon patch 76, a location beacon ortag 78, and a camera 79 to more efficiently operate the industrialautomation equipment 16. The sensors 58, 60, 62, 64, and 66 may be usedto determine ranges of expected measurements for each respective sensorand determine when measurements are outside of the range. The camera 79may be used to determine when objects near or a part of the industrialautomation equipment 16 have moved and/or when a hazard is present inthe process area 54. In some embodiments, the processor 68 may performpreventative actions, such as control the industrial automationequipment 16 or send an alert to the computing device 26, when themeasurements are outside the range. Also, the processor 36 may performactions based on the image/video data when any objects substantiallymoved and/or when a hazard is near the industrial automation equipment16. In additional embodiments, the data from the sensors and the cameramay be used to operate the industrial automation equipment 16 in somedesired way. Also, the location data obtained by the beacon patch 76 andthe location beacon or tag 78 may enable the computing device 26 todetermine directions to the multi-purpose sensing device 52 and/or theindustrial automation equipment 16 to which the multi-purpose sensingdevice 52 is attached.

In the preceding specification, various embodiments have been describedwith reference to the accompanying drawings. It will, however, beevident that various modifications and changes may be made thereto, andadditional embodiments may be implemented, without departing from thebroader scope of the present disclosure as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

1. A system comprising: a multi-purpose sensor configured to couple to amachine operating in an industrial environment, comprising: a cameraconfigured to obtain a first set of image data and a second set of imagedata comprising images of the machine and an environment surrounding themachine, wherein the first set of image data is associated with abaseline of the machine and the environment, and wherein the second setof image data is acquired subsequent to when the first set of image datais acquired; and a computing device configured to communicatively coupleto the multi-purpose sensor, wherein the computing device comprises: aprocessor configured to: receive the first set of image data and thesecond set of image data from the multi-purpose sensor; determine one ormore baseline positions of one or more objects in the first set of imagedata; determine one or more subsequent positions of the one or moreobjects in the second set of image data; determine whether thesubsequent positions vary from the baseline positions; and perform anaction when the subsequent positions vary from the baseline positions.2. The system of claim 1, wherein the first set of image data isacquired by the camera when the machine is operating normally.
 3. Thesystem of claim 1, wherein: the processor is configured to determinewhether a hazard is present within a proximity of the machine based onthe first set of image data and the second set of image data; andperform a second action when the hazard is present.
 4. The system ofclaim 1, wherein the multi-purpose sensor comprises a plurality ofsensors disposed within the multi-purpose sensor and configured toacquire a plurality of different types of sets of data associated withthe machine or the environment surrounding the machine, wherein: a firstportion of the plurality of different types of sets of data compriseshistorical sensor measurements over time when the machine is operatingnormally for each of the plurality of sensors; and a second portion ofthe plurality of different types of sets of data comprises sensormeasurements subsequent to when the first portion is acquired for eachof the plurality of sensors.
 5. The system of claim 1, wherein the firstset of image data and the second set of image data are taken within avisible light range or outside the visible light range.
 6. The system ofclaim 5, wherein the first set of image data and the second set of imagedata are taken within an infrared light range.
 7. The system of claim 6,wherein the first set of image data and the second set of image data aretaken within an ultraviolet light range.
 8. The system of claim 4,wherein the multi-purpose sensor comprises a second processor configuredto: determine a baseline individual measurement signature for each ofthe plurality of sensors based on the first portion of the plurality ofdifferent types of sets of data; determine a subsequent individualmeasurement signature based on the second portion of the plurality ofdifferent types of sets of data; determine whether the subsequentindividual measurement signature varies from the respective baselineindividual measurement signature; generate one or more signals when thesubsequent individual collective measurement signature varies from therespective baseline individual measurement signature, wherein the one ormore signals are configured to cause the computing device, a cloud-basedcomputing system, a control/monitoring device, or some combinationthereof to perform a preventative action, diagnostic, predictiveoperation, or some combination thereof; and transmit the one or moresignals to the computing device, the cloud-based computing system, thecontrol/monitoring device, or some combination thereof
 9. The system ofclaim 1, wherein the multi-purpose sensor comprises: a plurality ofsensors disposed within the multi-purpose sensor and configured toacquire a plurality of different types of sets of data associated withthe machine or the environment surrounding the machine, wherein theplurality of different types of sets of data comprises historical sensormeasurements for each of the plurality of sensors over time when themachine is operating efficiently; and a second processor configured to:determine a baseline individual measurement signature for each of theplurality of sensors based on the plurality of different types of setsof data, a collective measurement signature for the plurality of sensorsbased on the plurality of different types of sets of data, or both,wherein the baseline individual measurement signature and the collectivemeasurement signature are associated with when the machine is operatingefficiently; and determine one or more baseline machine settings, one ormore baseline machine operational parameters, or both associated withthe baseline individual measurement signature, the collectivemeasurement signature, or both.
 10. The system of claim 9, wherein thesecond processor is configured to operate the machine efficiently bymonitoring and modifying one or more current machine settings, one ormore current machine operational parameters, or both to match thebaseline machine settings, the baseline machine operational parameters,or both.
 11. The system of claim 9, wherein: determine one or moresubsequent individual measurement signatures for each of the pluralityof sensors, a subsequent collective measurement signature, or both;determine when any of the subsequent individual measurement signatures,the subsequent collective measurement signature, or both deviate fromthe baseline individual measurement signature, the collectivemeasurement signature, or both; and modify one or more current machinesettings, one or more current machine operational parameters, or both tocause the subsequent individual measurement signatures, the subsequentcollective measurement signature, or both to converge to the respectivebaseline individual measurement signatures, the collective measurementsignature, or both.
 12. The system of claim 1, wherein the multi-purposesensing device comprises a second processor configured to determine oneor more baseline machine settings, one or more baseline machineoperational parameters, or both when the machine is producing a desiredquantity of products with a certain level of quality based on a firstset of data acquired by a plurality of sensors disposed within themulti-purpose sensing device.
 13. The system of claim 12, wherein thesecond processor is configured to modify one or more current machinesettings, one or more current machine operational parameters, or bothwhen a second set of data acquired after the first set of data deviatesfrom the first set of data.
 14. A tangible, non-transitory computerreadable medium storing instructions that, when executed by a processor,are configured to cause the processor to: receive a first set of imagedata and a second set of image data from a multi-purpose sensor, whereinthe first and second sets of image data is obtained by a camera of themulti-purpose sensor, wherein the first and second sets of image datacomprise an image of a machine in an industrial automation application,an image of an environment surrounding the machine, or both, and whereinthe second set of image data is acquired subsequent to when the firstset of image data is acquired; determine one or more baseline positionsof one or more objects in the first set of image data; determine one ormore subsequent positions of the one or more objects in the second setof image data; determine whether the subsequent positions vary from thebaseline positions; and perform an action when the subsequent positionsvary from the baseline positions.
 15. The computer readable medium ofclaim 14, wherein the action comprises a preventative action, adiagnostic action, or a predictive operation, such that: thepreventative action comprises displaying an alert, sending an alert,disabling functionality of a software application, placing the machineoffline, or some combination thereof; the diagnostic action comprisesdetermining a cause for the subsequent positions varying from thebaseline positions; and the predictive operation comprises determiningone or more potential effects from the subsequent positions varying fromthe baseline positions on the machine or other industrial automationequipment.
 16. The computer readable medium of claim 14, wherein theinstructions, when executed by the processor, are configured to causethe processor to receive a signal from the multi-purpose sensor, whereinthe signal indicates that the machine is operating abnormally based onan audio signature that deviates from a baseline audio signatureobtained by an audio sensor of the multi-purpose sensor when the machinewas operating normally.
 17. The computer readable medium of claim 14,wherein the instructions, when executed by the processor, are configuredto cause the processor to determine whether a hazard exists in theenvironment based on the first set of image data and the second set ofimage data.
 18. The computer readable medium of claim 17, wherein theinstructions, when executed by a processor, are configured to cause theprocessor to perform a second action when the hazard exists.
 19. Amethod, comprising: receiving, via a processor, a first set of imagedata and a second set of image data from a multi-purpose sensor, whereinthe first and second sets of image data are obtained by a camera of themulti-purpose sensor, wherein the first and second sets of image datacomprise an image of a machine in an industrial automation application,an environment surrounding the machine, or both, and wherein the secondset of image data is acquired subsequent to when the first set of imagedata is acquired; determining, via the processor, one or more baselinepositions of one or more objects in the first set of image data;determining, via the processor, one or more subsequent positions of theone or more objects in the second set of image data; determining, viathe processor, whether the subsequent positions vary from the baselinepositions; and performing, via the processor, an action when thesubsequent positions vary from the baseline positions by a sufficientthreshold.
 20. The method of claim 19, comprising: receiving a set ofsensor measurements acquired by a plurality of sensors disposed withinthe multi-purpose sensor; and determining a reason for the subsequentpositions vary from the baseline positions based on the set of sensormeasurements.
 21. The method of claim 20, comprising displaying, via theprocessor, the reason.
 22. The method of claim 19, comprising:receiving, via the processor, a first set of sensor data and a secondset of sensor data from the multi-purpose sensor, wherein the first andsecond sets of sensor data is obtained by a plurality of sensorsdisposed within the multi-purpose sensor, and wherein the second set ofsensor data is acquired subsequent to when the first set of sensor datais acquired; determine one or more baseline measurement signatures forthe first set of sensor data; determine one or more subsequentmeasurement signatures for the second set of sensor data; determinewhether the subsequent measurements signatures vary from the baselinemeasurement signatures; and perform an action when the subsequentmeasurement signatures vary from the baseline measurement signatures bya sufficient threshold.
 23. The method of claim 19, comprisingdetermining, via the processor, when a hazard exists in the environmentbased on the first set of image data and the second set of image data.